Texture feature extraction for land-cover classification of remote sensing data in land consolidation district using semi-variogram analysis

The areas of the land consolidation projects are generally small, so the remote sensing images used in land-cover classification for the land consolidation are generally high spatial resolution images. The spectral complexity of land consolidation objects results in specific limitation using pixel-based analysis for land cover classification such as farmland, woodland, and water. Considering this problem, two approaches are compared in this study. One is the fixed window size co-occurrence texture extraction, and another is the changeable window size according to the result of semi-variogram analysis. Moreover, the methodology for optimizing the co-occurrence window size in terms of classification accuracy performance is introduced in this study. Zhaoquanying land consolidation project is selected as an example, which located in Shunyi District, Beijing, China; texture feature is extracted from SPOT5 remote sensing data in the TitanImage development environment and involved in classification. Accuracy assessment result shows that the classification accuracy has been improved effectively using the method introduced in this paper.

[1]  Tieniu Tan,et al.  Brief review of invariant texture analysis methods , 2002, Pattern Recognit..

[2]  Paolo Gamba,et al.  Semi-automatic choice of scale-dependent features for satellite SAR image classification , 2006, Pattern Recognit. Lett..

[3]  Jian Fan,et al.  Texture Classification by Wavelet Packet Signatures , 1993, MVA.

[4]  Matti Pietikäinen,et al.  Rotation-invariant texture classification using feature distributions , 2000, Pattern Recognit..

[5]  C. Woodcock,et al.  The use of variograms in remote sensing. I - Scene models and simulated images. II - Real digital images , 1988 .

[6]  Eulogio Pardo-Igúzquiza,et al.  VARIOG2D: a computer program for estimating the semi-variogram and its uncertainty , 2001 .

[7]  Luca Ridolfi,et al.  Preferential states of seasonal soil moisture: The impact of climate fluctuations , 2000 .

[8]  Sevkiye Sence Turk An analysis on the efficient applicability of the land readjustment (LR) method in Turkey , 2007 .

[9]  Mira Park,et al.  Hierarchical Indexing Images Using Weighted Low Dimensional Texture Features , 2001 .

[10]  C. Tien,et al.  Surface flatness of optical thin films evaluated by gray level co-occurrence matrix and entropy , 2008 .

[11]  T. Warner,et al.  SCALE AND TEXTURE IN DIGITAL IMAGE CLASSIFICATION , 2002 .

[12]  G. Matheron Principles of geostatistics , 1963 .

[13]  L. Ruiz,et al.  TEXTURE FEATURE EXTRACTION FOR CLASSIFICATION OF REMOTE SENSING DATA USING WAVELET DECOMPOSITION : A COMPARATIVE STUDY , 2004 .

[14]  Til Aach,et al.  Texture classification of gray-level images by multiscale cross co-occurrence matrices , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[15]  徐凯,et al.  Classification and Extraction of Urban Land-Use Information from High-Resolution Image Based on Object Multi-features , 2006 .

[16]  P. Curran The semivariogram in remote sensing: An introduction , 1988 .

[17]  Piotr W. Mirowski Retrieving Scale From Quasi-Stationary Images 1 / 11 Retrieving Scale from Quasi-Stationary Images , 2008 .

[18]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  T. C. Haas,et al.  Kriging and automated variogram modeling within a moving window , 1990 .

[20]  Peter I. Brooker Changes in dispersion variance consequent upon inaccurately modelled semi-variograms , 1988 .

[21]  M. Mancini,et al.  Retrieving Soil Moisture Over Bare Soil from ERS 1 Synthetic Aperture Radar Data: Sensitivity Analysis Based on a Theoretical Surface Scattering Model and Field Data , 1996 .

[22]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[23]  C Kong Classification and Extraction of Urban Land-Use Information from High-Resolution Image Based on Object Multi-features , 2006 .

[24]  Kee Tung. Wong,et al.  Texture features for image classification and retrieval. , 2002 .